Segementing and Clustering Neighborhoods in Toronto¶

Introduction¶

In this project, we will explore, segment, and cluster the neighboorhoods in the city of Toronto. The neighborhood data though is not readily available on the internate.

Toronto neighboorhood data is available on a wikipage, we can click here to see it. But we need to scrap the wiki page and wrangle the data, clean it and then convert it into a pandas dataframe.

The dataframe will consist of three columns: PostalCode, Borough, and Neighborhood. We only process the cells that have an assigned borough. We ignore cells with a borough that is Not assigned. More than one neighborhood can exist in one postal code area. For example, in the table on the Wikipedia page, you will notice that M5A is listed twice and has two neighborhoods: Harbourfront and Regent Park. These two rows will be combined into one row with the neighborhoods separated with a comma. If a cell has a borough but a Not assigned neighborhood, then the neighborhood will be the same as the borough. So for the 9th cell in the table on the Wikipedia page, the value of the Borough and the Neighborhood columns will be Queen's Park

There are different website scraping libraries and packages in Python. One of the most common packages is BeautifulSoup and we will use it in this project. Package's main documentation page: http://beautiful-soup-4.readthedocs.io/en/latest/

Lets import all the necessary libraries

import numpy as np # library to habndle data into vector
import pandas as pd # library for data analysis
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
import json # to handle json data
from  geopy.geocoders import Nominatim # to convert locations into longitude and lattitude
import requests #library to handle requests
from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe

# Matplotlib and associate plotting modules
import matplotlib.cm as cm
import matplotlib.colors as colors
# for clustering
from sklearn.cluster import KMeans
# map rendering library
import folium
from bs4 import BeautifulSoup
import lxml
print('Successfully, imported all libraries !')

Successfully, imported all libraries !

Download, scrap and wrangle¶

# download data and parse it:
r = requests.get('https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M')
soup = BeautifulSoup(r.text, 'html.parser')
table=soup.find('table', attrs={'class':'wikitable sortable'})

#get headers:
headers=table.findAll('th')
for i, head in enumerate(headers): headers[i]=str(headers[i]).replace("<th>","").replace("</th>","").replace("\n","")

#Find all items and skip first one:
rows=table.findAll('tr')
rows=rows[1:len(rows)]

# skip all meta symbols and line feeds between rows:
for i, row in enumerate(rows): rows[i] = str(rows[i]).replace("\n</td></tr>","").replace("<tr>\n<td>","")

# make dataframe, expand rows and drop the old one:
df=pd.DataFrame(rows)
df[headers] = df[0].str.split("</td>\n<td>", n = 2, expand = True) 
df.drop(columns=[0],inplace=True)

# skip not assigned boroughs:
df = df.drop(df[(df.Borough == "Not assigned")].index)
# give "Not assigned" Neighborhoods same name as Borough:
df.Neighborhood.replace("Not assigned", df.Borough, inplace=True)

# copy Borough value to Neighborhood if NaN:
df.Neighborhood.fillna(df.Borough, inplace=True)
# drop duplicate rows:
df=df.drop_duplicates()

# extract titles from columns
df.update(
    df.Neighborhood.loc[
        lambda x: x.str.contains('title')
    ].str.extract('title=\"([^\"]*)',expand=False))

df.update(
    df.Borough.loc[
        lambda x: x.str.contains('title')
    ].str.extract('title=\"([^\"]*)',expand=False))

# delete Toronto annotation from Neighbourhood:
df.update(
    df.Neighborhood.loc[
        lambda x: x.str.contains('Toronto')
    ].str.replace(", Toronto",""))
df.update(
    df.Neighborhood.loc[
        lambda x: x.str.contains('Toronto')
    ].str.replace("\(Toronto\)",""))

# combine multiple neighborhoods with the same post code
df2 = pd.DataFrame({'Postcode':df.Postcode.unique()})
df2['Borough']=pd.DataFrame(list(set(df['Borough'].loc[df['Postcode'] == x['Postcode']])) for i, x in df2.iterrows())
df2['Neighborhood']=pd.Series(list(set(df['Neighborhood'].loc[df['Postcode'] == x['Postcode']])) for i, x in df2.iterrows())
df2['Neighborhood']=df2['Neighborhood'].apply(lambda x: ', '.join(x))
df2.dtypes

df2

# add Geo-spatial data
dfll = pd.read_csv("Geospatial_Coordinates.csv")
dfll.rename(columns = {"Postal Code":"Postcode"}, inplace = True)
dfll.set_index("Postcode")
df2.set_index("Postcode")
toronto_data = pd.merge(df2, dfll)
toronto_data.head()

In order to define an instance of the geocoder, we need to define a user_agent. We will name our agent to_explorer, as shown below.

address = 'Toronto, ON, Canada'

geolocator = Nominatim(user_agent = "to_explorer", timeout = 10)
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude
print('The geograpical coordinate of Toronto, ON, Canada are {}, {}.'.format(latitude, longitude))

The geograpical coordinate of Toronto, ON, Canada are 43.653963, -79.387207.

Create a map of Toronto with neighborhoods superimposed on top.¶

# create map of toronto using latitude and longitude
map_toronto = folium.Map(location=[latitude, longitude], zoom_start=10)

# add markers to map
for lat, lng, borough, neighborhood in zip(toronto_data['Latitude'], toronto_data['Longitude'], toronto_data['Borough'], toronto_data['Neighborhood']):
    label = '{}, {}'.format(neighborhood, borough)
    label = folium.Popup(label, parse_html = True)
    folium.CircleMarker(
    [lat, lng],
    radius = 5,
    popup = label,
    fill = True,
    color = 'blue',
    fill_color='#3186cc',
    fill_opacity=0.7,
    parse_html=False    
    ).add_to(map_toronto)
map_toronto

# No. of borough in toronto
print("Total Number of borough is {}".format(len(toronto_data['Borough'].unique())))
print(toronto_data['Borough'][0])

Total Number of borough is 12
North York

For illustration purposes, let's simplify the above map and segment and cluster only the neighborhoods in North York. So let's slice the original dataframe and create a new dataframe of the Norh York data.

north_york = toronto_data[toronto_data['Borough']=='North York'].reset_index(drop = True)
north_york

Lets get Geographical coordinates of North York

address = 'North York, Toronto, Canada'

geolocator = Nominatim(user_agent="ny_explorer", timeout = 10)
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude
print('The geograpical coordinate of North York are {}, {}.'.format(latitude, longitude))

The geograpical coordinate of North York are 43.7543263, -79.4491169663959.

As we did with all of Toronto City, let's visualizat North York the neighborhoods in it.

# create map of North York using latitude and longitude values
map_northyork = folium.Map(location=[latitude, longitude], zoom_start=11)

# add markers to map
for lat, lng, label in zip(north_york['Latitude'], north_york['Longitude'], north_york['Neighborhood']):
    label = folium.Popup(label, parse_html=True)
    folium.CircleMarker(
        [lat, lng],
        radius=5,
        popup=label,
        color='yellow',
        fill=True,
        fill_color='#3186cc',
        fill_opacity=0.7,
        parse_html=False).add_to(map_northyork)  
    
map_northyork

Next, we are going to start utilizing the Foursquare API to explore the neighborhoods and segment them.

Define Foursquare Credentials and Version¶

CLIENT_ID = 'JNCN4XP0TKVMYFQGDGXW042J1EV3Q4YL4FHT0QJ3LX5MYQES' # your Foursquare ID
CLIENT_SECRET = 'DDUW2SV2EE15GWCCN1I1KNGLJURD2HYZBZPP5OETXFWBFHVC' # your Foursquare Secret
VERSION = '20180605' # Foursquare API version

print('Your credentails:')
print('CLIENT_ID: ' + CLIENT_ID)
print('CLIENT_SECRET:' + CLIENT_SECRET)

Your credentails:
CLIENT_ID: JNCN4XP0TKVMYFQGDGXW042J1EV3Q4YL4FHT0QJ3LX5MYQES
CLIENT_SECRET:DDUW2SV2EE15GWCCN1I1KNGLJURD2HYZBZPP5OETXFWBFHVC

Let's explore the first neighborhood in our dataframe.¶

Get the neighborhood's name.

north_york.loc[0, 'Neighborhood']

'Parkwoods'

Get the neighborhood's latitude and longitude values.

neighborhood_latitude = north_york.loc[0, 'Latitude'] # neighborhood latitude value
neighborhood_longitude = north_york.loc[0, 'Longitude'] # neighborhood longitude value

neighborhood_name = north_york.loc[0, 'Neighborhood'] # neighborhood name

print('Latitude and longitude values of {} are {}, {}.'.format(neighborhood_name, 
                                                               neighborhood_latitude, 
                                                               neighborhood_longitude))

Latitude and longitude values of Parkwoods are 43.7532586, -79.3296565.

Now, let's get the top 100 venues that are in Parkwoods within a radius of 500 meters.¶

First, let's create the GET request URL. Name your URL url.

# type your answer here
LIMIT = 100 # limit of number of venues returned by Foursquare API

radius = 500 # define radius

# create URL
url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(
    CLIENT_ID, 
    CLIENT_SECRET, 
    VERSION, 
    neighborhood_latitude, 
    neighborhood_longitude, 
    radius, 
    LIMIT)
url # display URL

'https://api.foursquare.com/v2/venues/explore?&client_id=JNCN4XP0TKVMYFQGDGXW042J1EV3Q4YL4FHT0QJ3LX5MYQES&client_secret=DDUW2SV2EE15GWCCN1I1KNGLJURD2HYZBZPP5OETXFWBFHVC&v=20180605&ll=43.7532586,-79.3296565&radius=500&limit=100'

Send the GET request and examine the resutls

results = requests.get(url).json()
venues = results['response']['groups'][0]['items']
nearby_venues = json_normalize(venues)
nearby_venues

From the Foursquare lab in the previous module, we know that all the information is in the items key. Before we proceed, let's borrow the get_category_type function from the Foursquare lab.

# function that extracts the category of the venue
def get_category_type(row):
    try:
        categories_list = row['categories']
    except:
        categories_list = row['venue.categories']
        
    if len(categories_list) == 0:
        return None
    else:
        return categories_list[0]['name']

Now we are ready to clean the json and structure it into a pandas dataframe.

venues = results['response']['groups'][0]['items']
    
nearby_venues = json_normalize(venues) # flatten JSON

# filter columns
filtered_columns = ['venue.name', 'venue.categories', 'venue.location.lat', 'venue.location.lng']
nearby_venues = nearby_venues.loc[:, filtered_columns]

# filter the category for each row
nearby_venues['venue.categories'] = nearby_venues.apply(get_category_type, axis=1)

# clean columns
nearby_venues.columns = [col.split(".")[-1] for col in nearby_venues.columns]

nearby_venues

And how many venues were returned by Foursquare

print('{} venues were returned by Foursquare.'.format(nearby_venues.shape[0]))

3 venues were returned by Foursquare.

2. Explore Neighborhoods in North York¶

Let's create a function to repeat the same process to all the neighborhoods in North York¶

def getNearbyVenues(names, latitudes, longitudes, radius=500):
    
    venues_list=[]
    for name, lat, lng in zip(names, latitudes, longitudes):
        print(name)
            
        # create the API request URL
        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(
            CLIENT_ID, 
            CLIENT_SECRET, 
            VERSION, 
            lat, 
            lng, 
            radius, 
            LIMIT)
            
        # make the GET request
        results = requests.get(url).json()["response"]['groups'][0]['items']
        
        # return only relevant information for each nearby venue
        venues_list.append([(
            name, 
            lat, 
            lng, 
            v['venue']['name'], 
            v['venue']['location']['lat'], 
            v['venue']['location']['lng'],  
            v['venue']['categories'][0]['name']) for v in results])

    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])
    nearby_venues.columns = ['Neighborhood', 
                  'Neighborhood Latitude', 
                  'Neighborhood Longitude', 
                  'Venue', 
                  'Venue Latitude', 
                  'Venue Longitude', 
                  'Venue Category']
    
    return(nearby_venues)

Now write the code to run the above function on each neighborhood and create a new dataframe called northyork_venues.¶

# type your answer here

northyork_venues = getNearbyVenues(names=north_york['Neighborhood'],
                                   latitudes=north_york['Latitude'],
                                   longitudes=north_york['Longitude']
                                  )

Parkwoods
Victoria Village
Lawrence Heights, Lawrence Manor
Don Mills North
Glencairn
Flemingdon Park, Don Mills South
Hillcrest Village
Wilson Heights, Bathurst Manor, Downsview North
Henry Farm, Fairview, Oriole
Northwood Park, York University
Bayview Village
Downsview East, CFB Toronto
Silver Hills, York Mills
Downsview
North Park, Downsview, Upwood Park
Humber Summit
Newtonbrook, Willowdale
Downsview Central
Bedford Park, Lawrence Manor East
Humberlea, Emery
Willowdale South
Downsview Northwest
York Mills West
Willowdale West

Let's check the size of the resulting dataframe¶

print(northyork_venues.shape)
northyork_venues.head()

(240, 7)

Let's check how many venues were returned for each neighborhood

northyork_venues.groupby('Neighborhood').count().iloc[:, 0]

Neighborhood
Bayview Village                                     4
Bedford Park, Lawrence Manor East                  24
Don Mills North                                     5
Downsview                                           4
Downsview Central                                   2
Downsview East, CFB Toronto                         2
Downsview Northwest                                 4
Flemingdon Park, Don Mills South                   22
Glencairn                                           4
Henry Farm, Fairview, Oriole                       64
Hillcrest Village                                   4
Humber Summit                                       2
Humberlea, Emery                                    3
Lawrence Heights, Lawrence Manor                   11
North Park, Downsview, Upwood Park                  4
Northwood Park, York University                     5
Parkwoods                                           3
Victoria Village                                    6
Willowdale South                                   36
Willowdale West                                     6
Wilson Heights, Bathurst Manor, Downsview North    21
York Mills West                                     4
Name: Neighborhood Latitude, dtype: int64

Let's find out how many unique categories can be curated from all the returned venues¶

print('There are {} uniques categories.'.format(len(northyork_venues['Venue Category'].unique())))

There are 103 uniques categories.

3. Analyze Each Neighborhood¶

northyork_onehot = pd.get_dummies(northyork_venues[['Venue Category']], prefix="", prefix_sep="")
northyork_onehot

# one hot encoding
northyork_onehot = pd.get_dummies(northyork_venues[['Venue Category']], prefix="", prefix_sep="")

# add neighborhood column back to dataframe
northyork_onehot['Neighborhood'] = northyork_venues['Neighborhood'] 

# move neighborhood column to the first column
fixed_columns = [northyork_onehot.columns[-1]] + list(northyork_onehot.columns[:-1])
northyork_onehot = northyork_onehot[fixed_columns]

northyork_onehot.head()

And let's examine the new dataframe size.

northyork_onehot.shape

(240, 104)

Next, let's group rows by neighborhood and by taking the mean of the frequency of occurrence of each category¶

northyork_grouped = northyork_onehot.groupby('Neighborhood').mean().reset_index()
northyork_grouped

Let's confirm the new size¶

Let's print each neighborhood along with the top 3 most common venues¶

num_top_venues = 3

for hood in northyork_grouped['Neighborhood']:
    print("----"+hood+"----")
    temp = northyork_grouped[northyork_grouped['Neighborhood'] == hood].T.reset_index()
    temp.columns = ['venue','freq']
    temp = temp.iloc[1:]
    temp['freq'] = temp['freq'].astype(float)
    temp = temp.round({'freq': 2})
    print(temp.sort_values('freq', ascending=False).reset_index(drop=True).head(num_top_venues))
    print('\n')

----Bayview Village----
                 venue  freq
0                 Bank  0.25
1   Chinese Restaurant  0.25
2  Japanese Restaurant  0.25


----Bedford Park, Lawrence Manor East----
                  venue  freq
0    Italian Restaurant  0.08
1  Fast Food Restaurant  0.08
2        Sandwich Place  0.08


----Don Mills North----
                  venue  freq
0  Gym / Fitness Center   0.2
1        Baseball Field   0.2
2   Japanese Restaurant   0.2


----Downsview----
   venue  freq
0  Hotel  0.25
1   Bank  0.25
2   Park  0.25


----Downsview Central----
               venue  freq
0     Baseball Field   0.5
1         Food Truck   0.5
2  Accessories Store   0.0


----Downsview East, CFB Toronto----
                  venue  freq
0               Airport   0.5
1                  Park   0.5
2  Gym / Fitness Center   0.0


----Downsview Northwest----
           venue  freq
0            Gym  0.25
1  Grocery Store  0.25
2   Liquor Store  0.25


----Flemingdon Park, Don Mills South----
              venue  freq
0               Gym  0.09
1  Asian Restaurant  0.09
2        Beer Store  0.09


----Glencairn----
         venue  freq
0          Pub  0.25
1  Pizza Place  0.25
2         Park  0.25


----Henry Farm, Fairview, Oriole----
                  venue  freq
0        Clothing Store  0.16
1  Fast Food Restaurant  0.08
2           Coffee Shop  0.08


----Hillcrest Village----
                      venue  freq
0                      Pool  0.25
1  Mediterranean Restaurant  0.25
2               Golf Course  0.25


----Humber Summit----
                 venue  freq
0          Pizza Place   0.5
1  Empanada Restaurant   0.5
2    Accessories Store   0.0


----Humberlea, Emery----
                           venue  freq
0     Construction & Landscaping  0.33
1  Paper / Office Supplies Store  0.33
2                 Baseball Field  0.33


----Lawrence Heights, Lawrence Manor----
                    venue  freq
0  Furniture / Home Store  0.27
1       Accessories Store  0.09
2   Vietnamese Restaurant  0.09


----North Park, Downsview, Upwood Park----
                        venue  freq
0  Construction & Landscaping  0.25
1                      Bakery  0.25
2                       Trail  0.25


----Northwood Park, York University----
           venue  freq
0  Metro Station   0.2
1    Coffee Shop   0.2
2            Bar   0.2


----Parkwoods----
               venue  freq
0  Food & Drink Shop  0.33
1               Park  0.33
2  Convenience Store  0.33


----Victoria Village----
                   venue  freq
0  Portuguese Restaurant  0.17
1           Intersection  0.17
2            Pizza Place  0.17


----Willowdale South----
              venue  freq
0  Ramen Restaurant  0.08
1  Sushi Restaurant  0.08
2              Café  0.06


----Willowdale West----
           venue  freq
0   Home Service  0.17
1    Pizza Place  0.17
2  Grocery Store  0.17


----Wilson Heights, Bathurst Manor, Downsview North----
                venue  freq
0         Coffee Shop  0.10
1  Frozen Yogurt Shop  0.05
2         Supermarket  0.05


----York Mills West----
               venue  freq
0               Park  0.50
1               Bank  0.25
2  Convenience Store  0.25

Let's put that into a pandas dataframe¶

First, let's write a function to sort the venues in descending order.

def return_most_common_venues(row, num_top_venues):
    row_categories = row.iloc[1:]
    row_categories_sorted = row_categories.sort_values(ascending=False)
    
    return row_categories_sorted.index.values[0:num_top_venues]

Now Explore Each of Neighborhoods of Toronto¶

Let's create a function to repeat the same process to all the neighborhoods.¶

def getNearbyVenues(names, latitudes, longitudes, radius=500):
    
    venues_list=[]
    for name, lat, lng in zip(names, latitudes, longitudes):
        print(name)
            
        # create the API request URL
        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(
            CLIENT_ID, 
            CLIENT_SECRET, 
            VERSION, 
            lat, 
            lng, 
            radius, 
            LIMIT)
            
        # make the GET request
        results = requests.get(url).json()["response"]['groups'][0]['items']
        
        # return only relevant information for each nearby venue
        venues_list.append([(
            name, 
            lat, 
            lng, 
            v['venue']['name'], 
            v['venue']['location']['lat'], 
            v['venue']['location']['lng'],  
            v['venue']['categories'][0]['name']) for v in results])

    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])
    nearby_venues.columns = ['Neighborhood', 
                  'Neighborhood Latitude', 
                  'Neighborhood Longitude', 
                  'Venue', 
                  'Venue Latitude', 
                  'Venue Longitude', 
                  'Venue Category']
    
    return(nearby_venues)

Now write the code to run the above function on each neighborhood and create a new dataframe called manhattan_venues.¶

# type your answer here

toronto_venues = getNearbyVenues(names=toronto_data['Neighborhood'],
                                   latitudes=toronto_data['Latitude'],
                                   longitudes=toronto_data['Longitude']
                                  )

Parkwoods
Victoria Village
Regent Park
Lawrence Heights, Lawrence Manor
Queen's Park 
Queen's Park 
Rouge, Malvern
Don Mills North
Woodbine Gardens, Parkview Hill
Ryerson, Garden District
Glencairn
Cloverdale, West Deane Park, Martin Grove, Islington, Princess Gardens
Port Union, Highland Creek , Rouge Hill
Flemingdon Park, Don Mills South
Woodbine Heights
St. James Town
Humewood-Cedarvale
Markland Wood, Eringate, Old Burnhamthorpe, Bloordale Gardens
Morningside, Guildwood, West Hill
The Beaches
Berczy Park
Caledonia-Fairbanks
Woburn
Leaside
Central Bay Street
Christie
Cedarbrae
Hillcrest Village
Wilson Heights, Bathurst Manor, Downsview North
Thorncliffe Park
Adelaide, King, Richmond
Dovercourt Village, Dufferin
Scarborough Village
Henry Farm, Fairview, Oriole
Northwood Park, York University
East Toronto
Toronto Islands, Harbourfront East, Union Station 
Little Portugal, Trinity–Bellwoods
Kennedy Park, East Birchmount Park, Ionview
Bayview Village
Downsview East, CFB Toronto
Riverdale, The Danforth West
Toronto Dominion Centre, Design Exchange
Brockton, Parkdale Village, Exhibition Place
Golden Mile, Oakridge, Clairlea
Silver Hills, York Mills
Downsview
India Bazaar, The Beaches West
Commerce Court, Victoria Hotel
North Park, Downsview, Upwood Park
Humber Summit
Cliffcrest, Cliffside, Scarborough Village West
Newtonbrook, Willowdale
Downsview Central
Studio District
Bedford Park, Lawrence Manor East
Del Ray, Mount Dennis, Keelesdale, Silverthorn
Humberlea, Emery
Cliffside West, Birch Cliff
Willowdale South
Downsview Northwest
Lawrence Park
Roselawn
The Junction North, Runnymede
Weston
Wexford Heights, Scarborough Town Centre, Dorset Park
York Mills West
Davisville North
Forest Hill West, Forest Hill North
High Park, The Junction South
Westmount
Wexford, Maryvale
Willowdale West
North Toronto West
The Annex, North Midtown, Yorkville
Roncesvalles, Parkdale
Canada Post Gateway Processing Centre
Kingsview Village, Richview Gardens, Martin Grove Gardens, St. Phillips
Agincourt
Davisville
University of Toronto, Harbord
Swansea, Runnymede
Clarks Corners, Sullivan, Tam O'Shanter – Sullivan
Moore Park, Summerhill East
Chinatown, Kensington Market, Grange Park 
Agincourt North, Steeles East, Milliken, Ontario, L'Amoreaux East
South Hill, Summerhill West, Deer Park, Forest Hill SE, Rathnelly
CN Tower, King and Spadina, Harbourfront West, Bathurst Quay, Railway Lands, South Niagara, Island airport
New Toronto, Mimico South, Humber Bay Shores
Beaumond Heights, Albion Gardens, Silverstone, Humbergate, Thistletown, South Steeles, Mount Olive-Silverstone-Jamestown
L'Amoreaux West
Rosedale
Stn A PO Boxes 25 The Esplanade
Alderwood, Long Branch
Northwest
Upper Rouge
Cabbagetown, St. James Town
Underground city, First Canadian Place
The Kingsway, Old Mill North, Montgomery Road
Church and Wellesley
Business Reply Mail Processing Centre 969 Eastern
Kingsway Park South East, Humber Bay, Old Mill, The Queensway, Fairmont Royal York Hotel, Mimico, Sunnylea, King's Mill Park
Royal York South West, South of Bloor, The Queensway, Kingsway Park South West, Mimico

print(toronto_venues.shape)
toronto_venues.head()

(2227, 7)

Let's check how many venues were returned for each neighborhood

toronto_venues.groupby('Neighborhood').count()

Let's find out how many unique categories can be curated from all the returned venu¶

print('There are {} uniques categories.'.format(len(toronto_venues['Venue Category'].unique())))

There are 267 uniques categories.

Analyze Each Neighborhood¶

# one hot encoding
toronto_onehot = pd.get_dummies(toronto_venues[['Venue Category']], prefix="", prefix_sep="")

# add neighborhood column back to dataframe
toronto_onehot['Neighborhood'] = toronto_venues['Neighborhood'] 

# move neighborhood column to the first column
toronto_onehot.set_index("Neighborhood", inplace = True)

toronto_onehot.reset_index()

Next, let's group rows by neighborhood and by taking the mean of the frequency of occurrence of each category¶

toronto_grouped = toronto_onehot.groupby('Neighborhood').mean().reset_index()
toronto_grouped

Let's confirm the new size¶

toronto_grouped.shape

(98, 267)

Let's print each neighborhood along with the top 5 most common venues¶

num_top_venues = 3

for hood in toronto_grouped['Neighborhood']:
    print("----"+hood+"----")
    temp = toronto_grouped[toronto_grouped['Neighborhood'] == hood].T.reset_index()
    temp.columns = ['venue','freq']
    temp = temp.iloc[1:]
    temp['freq'] = temp['freq'].astype(float)
    temp = temp.round({'freq': 2})
    print(temp.sort_values('freq', ascending=False).reset_index(drop=True).head(num_top_venues))
    print('\n')

----Adelaide, King, Richmond----
         venue  freq
0  Coffee Shop  0.08
1         Café  0.05
2   Steakhouse  0.04


----Agincourt----
                       venue  freq
0             Breakfast Spot  0.25
1  Latin American Restaurant  0.25
2                     Lounge  0.25


----Agincourt North, Steeles East, Milliken, Ontario, L'Amoreaux East----
               venue  freq
0               Park   0.5
1         Playground   0.5
2  Accessories Store   0.0


----Alderwood, Long Branch----
         venue  freq
0  Pizza Place  0.22
1     Pharmacy  0.11
2  Coffee Shop  0.11


----Bayview Village----
                venue  freq
0                Bank  0.25
1  Chinese Restaurant  0.25
2                Café  0.25


----Beaumond Heights, Albion Gardens, Silverstone, Humbergate, Thistletown, South Steeles, Mount Olive-Silverstone-Jamestown----
            venue  freq
0   Grocery Store  0.25
1        Pharmacy  0.12
2  Sandwich Place  0.12


----Bedford Park, Lawrence Manor East----
                  venue  freq
0  Fast Food Restaurant  0.08
1    Italian Restaurant  0.08
2           Coffee Shop  0.08


----Berczy Park----
          venue  freq
0   Coffee Shop  0.09
1  Cocktail Bar  0.05
2      Beer Bar  0.04


----Brockton, Parkdale Village, Exhibition Place----
            venue  freq
0            Café  0.14
1  Breakfast Spot  0.09
2     Coffee Shop  0.09


----Business Reply Mail Processing Centre 969 Eastern----
                venue  freq
0  Light Rail Station  0.12
1         Yoga Studio  0.06
2       Auto Workshop  0.06


----CN Tower, King and Spadina, Harbourfront West, Bathurst Quay, Railway Lands, South Niagara, Island airport----
              venue  freq
0    Airport Lounge  0.13
1   Airport Service  0.13
2  Airport Terminal  0.13


----Cabbagetown, St. James Town----
         venue  freq
0  Pizza Place  0.06
1  Coffee Shop  0.06
2         Park  0.04


----Caledonia-Fairbanks----
                  venue  freq
0                  Park   0.4
1         Women's Store   0.2
2  Fast Food Restaurant   0.2


----Canada Post Gateway Processing Centre----
            venue  freq
0           Hotel   0.2
1     Coffee Shop   0.2
2  Sandwich Place   0.1


----Cedarbrae----
                venue  freq
0              Bakery  0.12
1  Athletics & Sports  0.12
2         Gas Station  0.12


----Central Bay Street----
                venue  freq
0         Coffee Shop  0.15
1                Café  0.07
2  Italian Restaurant  0.05


----Chinatown, Kensington Market, Grange Park ----
                           venue  freq
0                           Café  0.05
1          Vietnamese Restaurant  0.05
2  Vegetarian / Vegan Restaurant  0.05


----Christie----
           venue  freq
0           Café  0.18
1  Grocery Store  0.18
2           Park  0.12


----Church and Wellesley----
                 venue  freq
0          Coffee Shop  0.10
1  Japanese Restaurant  0.05
2              Gay Bar  0.05


----Clarks Corners, Sullivan, Tam O'Shanter – Sullivan----
             venue  freq
0      Pizza Place   0.2
1  Thai Restaurant   0.1
2     Noodle House   0.1


----Cliffcrest, Cliffside, Scarborough Village West----
                 venue  freq
0                Motel  0.33
1         Intersection  0.33
2  American Restaurant  0.33


----Cliffside West, Birch Cliff----
                   venue  freq
0        College Stadium  0.25
1                   Café  0.25
2  General Entertainment  0.25


----Commerce Court, Victoria Hotel----
         venue  freq
0  Coffee Shop  0.11
1         Café  0.07
2        Hotel  0.06


----Davisville----
            venue  freq
0    Dessert Shop  0.10
1  Sandwich Place  0.10
2     Pizza Place  0.06


----Davisville North----
               venue  freq
0  Convenience Store  0.11
1                Gym  0.11
2     Breakfast Spot  0.11


----Del Ray, Mount Dennis, Keelesdale, Silverthorn----
            venue  freq
0  Discount Store  0.25
1      Restaurant  0.25
2  Sandwich Place  0.25


----Don Mills North----
                  venue  freq
0        Baseball Field   0.2
1  Gym / Fitness Center   0.2
2   Japanese Restaurant   0.2


----Dovercourt Village, Dufferin----
         venue  freq
0       Bakery  0.13
1     Pharmacy  0.13
2  Supermarket  0.13


----Downsview----
           venue  freq
0  Shopping Mall  0.25
1           Park  0.25
2           Bank  0.25


----Downsview Central----
               venue  freq
0     Baseball Field   0.5
1         Food Truck   0.5
2  Accessories Store   0.0


----Downsview East, CFB Toronto----
         venue  freq
0      Airport   0.5
1         Park   0.5
2  Men's Store   0.0


----Downsview Northwest----
                  venue  freq
0          Liquor Store  0.25
1  Gym / Fitness Center  0.25
2                   Gym  0.25


----East Toronto----
               venue  freq
0               Park  0.50
1  Convenience Store  0.25
2        Coffee Shop  0.25


----Flemingdon Park, Don Mills South----
              venue  freq
0        Beer Store  0.09
1               Gym  0.09
2  Asian Restaurant  0.09


----Forest Hill West, Forest Hill North----
              venue  freq
0  Sushi Restaurant  0.25
1             Trail  0.25
2          Bus Line  0.25


----Glencairn----
                 venue  freq
0                 Park  0.25
1  Japanese Restaurant  0.25
2                  Pub  0.25


----Golden Mile, Oakridge, Clairlea----
                  venue  freq
0                Bakery   0.2
1              Bus Line   0.2
2  Fast Food Restaurant   0.1


----Henry Farm, Fairview, Oriole----
                  venue  freq
0        Clothing Store  0.16
1           Coffee Shop  0.08
2  Fast Food Restaurant  0.08


----High Park, The Junction South----
                venue  freq
0                 Bar  0.08
1                Café  0.08
2  Mexican Restaurant  0.08


----Hillcrest Village----
                      venue  freq
0  Mediterranean Restaurant  0.25
1                      Pool  0.25
2               Golf Course  0.25


----Humber Summit----
                 venue  freq
0  Empanada Restaurant   0.5
1          Pizza Place   0.5
2          Men's Store   0.0


----Humberlea, Emery----
                           venue  freq
0     Construction & Landscaping  0.33
1  Paper / Office Supplies Store  0.33
2                 Baseball Field  0.33


----Humewood-Cedarvale----
          venue  freq
0         Field  0.25
1  Hockey Arena  0.25
2         Trail  0.25


----India Bazaar, The Beaches West----
            venue  freq
0  Sandwich Place  0.11
1     Pizza Place  0.05
2  Ice Cream Shop  0.05


----Kennedy Park, East Birchmount Park, Ionview----
              venue  freq
0    Discount Store   0.4
1       Coffee Shop   0.2
2  Department Store   0.2


----Kingsview Village, Richview Gardens, Martin Grove Gardens, St. Phillips----
            venue  freq
0            Park  0.25
1     Pizza Place  0.25
2  Sandwich Place  0.25


----Kingsway Park South East, Humber Bay, Old Mill, The Queensway, Fairmont Royal York Hotel, Mimico, Sunnylea, King's Mill Park----
                        venue  freq
0                   Locksmith  0.33
1  Construction & Landscaping  0.33
2              Baseball Field  0.33


----L'Amoreaux West----
                  venue  freq
0  Fast Food Restaurant  0.15
1    Chinese Restaurant  0.15
2        Breakfast Spot  0.08


----Lawrence Heights, Lawrence Manor----
                    venue  freq
0  Furniture / Home Store  0.27
1   Vietnamese Restaurant  0.09
2      Miscellaneous Shop  0.09


----Lawrence Park----
         venue  freq
0         Park  0.33
1  Swim School  0.33
2     Bus Line  0.33


----Leaside----
                 venue  freq
0  Sporting Goods Shop  0.09
1          Coffee Shop  0.09
2         Burger Joint  0.06


----Little Portugal, Trinity–Bellwoods----
              venue  freq
0               Bar  0.15
1  Asian Restaurant  0.05
2        Restaurant  0.05


----Markland Wood, Eringate, Old Burnhamthorpe, Bloordale Gardens----
               venue  freq
0           Pharmacy  0.12
1        Pizza Place  0.12
2  Convenience Store  0.12


----Moore Park, Summerhill East----
          venue  freq
0          Park  0.33
1    Restaurant  0.33
2  Tennis Court  0.33


----Morningside, Guildwood, West Hill----
            venue  freq
0  Breakfast Spot  0.12
1  Medical Center  0.12
2             Spa  0.12


----New Toronto, Mimico South, Humber Bay Shores----
         venue  freq
0  Coffee Shop  0.14
1         Café  0.14
2       Bakery  0.07


----North Park, Downsview, Upwood Park----
                        venue  freq
0  Construction & Landscaping  0.25
1                        Park  0.25
2                      Bakery  0.25


----North Toronto West----
                 venue  freq
0       Clothing Store  0.14
1  Sporting Goods Shop  0.10
2          Coffee Shop  0.10


----Northwest----
                 venue  freq
0                  Bar  0.33
1            Drugstore  0.33
2  Rental Car Location  0.33


----Northwood Park, York University----
                  venue  freq
0  Caribbean Restaurant   0.2
1        Massage Studio   0.2
2                   Bar   0.2


----Parkwoods----
               venue  freq
0               Park  0.33
1  Convenience Store  0.33
2  Food & Drink Shop  0.33


----Port Union, Highland Creek , Rouge Hill----
               venue  freq
0                Bar   1.0
1  Accessories Store   0.0
2      Metro Station   0.0


----Queen's Park ----
         venue  freq
0  Coffee Shop  0.29
1         Park  0.05
2          Gym  0.05


----Regent Park----
         venue  freq
0  Coffee Shop  0.18
1         Park  0.07
2       Bakery  0.07


----Riverdale, The Danforth West----
              venue  freq
0  Greek Restaurant  0.21
1       Coffee Shop  0.10
2    Ice Cream Shop  0.07


----Roncesvalles, Parkdale----
                venue  freq
0           Gift Shop  0.15
1  Italian Restaurant  0.08
2        Dessert Shop  0.08


----Rosedale----
        venue  freq
0        Park  0.50
1  Playground  0.25
2       Trail  0.25


----Roselawn----
               venue  freq
0             Garden   1.0
1  Accessories Store   0.0
2   Malay Restaurant   0.0


----Rouge, Malvern----
                             venue  freq
0             Fast Food Restaurant   1.0
1                Accessories Store   0.0
2  Molecular Gastronomy Restaurant   0.0


----Royal York South West, South of Bloor, The Queensway, Kingsway Park South West, Mimico----
               venue  freq
0  Convenience Store  0.08
1      Grocery Store  0.08
2       Burger Joint  0.08


----Ryerson, Garden District----
            venue  freq
0     Coffee Shop  0.09
1  Clothing Store  0.06
2            Café  0.04


----Scarborough Village----
                      venue  freq
0                       Spa   0.5
1                Playground   0.5
2  Mediterranean Restaurant   0.0


----South Hill, Summerhill West, Deer Park, Forest Hill SE, Rathnelly----
                venue  freq
0                 Pub  0.12
1         Coffee Shop  0.12
2  Light Rail Station  0.06


----St. James Town----
         venue  freq
0         Café  0.06
1  Coffee Shop  0.06
2   Restaurant  0.05


----Stn A PO Boxes 25 The Esplanade----
         venue  freq
0  Coffee Shop  0.12
1         Café  0.04
2   Restaurant  0.04


----Studio District----
                venue  freq
0                Café  0.10
1         Coffee Shop  0.07
2  Italian Restaurant  0.05


----Swansea, Runnymede----
                venue  freq
0                Café  0.08
1         Coffee Shop  0.08
2  Italian Restaurant  0.05


----The Annex, North Midtown, Yorkville----
            venue  freq
0  Sandwich Place  0.14
1            Café  0.14
2     Coffee Shop  0.09


----The Beaches----
               venue  freq
0                Pub  0.25
1  Health Food Store  0.25
2              Trail  0.25


----The Junction North, Runnymede----
           venue  freq
0    Pizza Place   0.2
1  Grocery Store   0.2
2        Brewery   0.2


----The Kingsway, Old Mill North, Montgomery Road----
        venue  freq
0        Park  0.25
1       River  0.25
2  Smoke Shop  0.25


----Thorncliffe Park----
               venue  freq
0  Indian Restaurant  0.11
1       Burger Joint  0.11
2        Yoga Studio  0.05


----Toronto Dominion Centre, Design Exchange----
         venue  freq
0  Coffee Shop  0.14
1         Café  0.08
2        Hotel  0.07


----Toronto Islands, Harbourfront East, Union Station ----
         venue  freq
0  Coffee Shop  0.12
1     Aquarium  0.05
2         Café  0.04


----Underground city, First Canadian Place----
         venue  freq
0  Coffee Shop  0.12
1         Café  0.07
2          Gym  0.04


----University of Toronto, Harbord----
                 venue  freq
0                 Café  0.16
1            Bookstore  0.08
2  Japanese Restaurant  0.05


----Victoria Village----
               venue  freq
0        Pizza Place  0.17
1  French Restaurant  0.17
2       Hockey Arena  0.17


----Westmount----
                       venue  freq
0                Pizza Place  0.25
1                Coffee Shop  0.12
2  Middle Eastern Restaurant  0.12


----Weston----
                      venue  freq
0                      Park   1.0
1         Accessories Store   0.0
2  Mediterranean Restaurant   0.0


----Wexford Heights, Scarborough Town Centre, Dorset Park----
                venue  freq
0   Indian Restaurant  0.33
1           Pet Store  0.17
2  Light Rail Station  0.17


----Wexford, Maryvale----
               venue  freq
0  Accessories Store  0.12
1         Smoke Shop  0.12
2     Sandwich Place  0.12


----Willowdale South----
              venue  freq
0  Sushi Restaurant  0.08
1  Ramen Restaurant  0.08
2              Café  0.06


----Willowdale West----
           venue  freq
0       Pharmacy  0.17
1    Pizza Place  0.17
2  Grocery Store  0.17


----Wilson Heights, Bathurst Manor, Downsview North----
         venue  freq
0  Coffee Shop  0.10
1  Pizza Place  0.05
2  Bridal Shop  0.05


----Woburn----
               venue  freq
0        Coffee Shop  0.50
1  Korean Restaurant  0.25
2           Pharmacy  0.25


----Woodbine Gardens, Parkview Hill----
                  venue  freq
0           Pizza Place  0.17
1  Fast Food Restaurant  0.17
2    Athletics & Sports  0.08


----Woodbine Heights----
            venue  freq
0  Cosmetics Shop   0.1
1      Beer Store   0.1
2        Pharmacy   0.1


----York Mills West----
               venue  freq
0               Park  0.50
1  Convenience Store  0.25
2               Bank  0.25

Let's put that into a pandas dataframe¶

First, let's write a function to sort the venues in descending order.

def return_most_common_venues(row, num_top_venues):
    row_categories = row.iloc[1:]
    row_categories_sorted = row_categories.sort_values(ascending=False)
    
    return row_categories_sorted.index.values[0:num_top_venues]

Now let's create the new dataframe and display the top 10 venues for each neighborhood.

num_top_venues = 10

indicators = ['st', 'nd', 'rd']

# create columns according to number of top venues
columns = ['Neighborhood']
for ind in np.arange(num_top_venues):
    try:
        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))
    except:
        columns.append('{}th Most Common Venue'.format(ind+1))

# create a new dataframe
neighborhoods_venues_sorted = pd.DataFrame(columns=columns)
neighborhoods_venues_sorted['Neighborhood'] = toronto_grouped['Neighborhood']

for ind in np.arange(toronto_grouped.shape[0]):
    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(toronto_grouped.iloc[ind, :], num_top_venues)

neighborhoods_venues_sorted.head()

4. Cluster Neighborhoods¶

Run k-means to cluster the neighborhood into 5 clusters.

# set number of clusters
kclusters = 5

toronto_grouped_clustering = toronto_grouped.drop('Neighborhood', 1)

# run k-means clustering
kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(toronto_grouped_clustering)

# check cluster labels generated for each row in the dataframe
kmeans.labels_[0:10]

array([4, 4, 0, 4, 4, 4, 4, 4, 4, 4])

Let's create a new dataframe that includes the cluster as well as the top 10 venues for each neighborhood.

# add clustering labels
neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)

toronto_merged = toronto_data

# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood
toronto_merged = toronto_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')



# check the last columns!
toronto_merged.head()

Finally, let's visualize the resulting clusters

# create map
map_clusters = folium.Map(location=[latitude, longitude], zoom_start=11)

# set color scheme for the clusters
x = np.arange(kclusters)
ys = [i + x + (i*x)**2 for i in range(kclusters)]
colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))
rainbow = [colors.rgb2hex(i) for i in colors_array]

# add markers to the map
markers_colors = []
for lat, lon, poi, cluster in zip(toronto_merged['Latitude'], toronto_merged['Longitude'], toronto_merged['Neighborhood'], toronto_merged['Cluster Labels']):
    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)
    folium.CircleMarker(
        [lat, lon],
        radius=5,
        popup=label,
        #color=rainbow[cluster-1],
        fill=True,
        #fill_color=rainbow[cluster-1],
        fill_opacity=0.7).add_to(map_clusters)
       
map_clusters

5. Examine Clusters¶

Now, you can examine each cluster and determine the discriminating venue categories that distinguish each cluster. Based on the defining categories, you can then assign a name to each cluster. I will leave this exercise to you.

Cluster 1¶

toronto_merged.loc[toronto_merged['Cluster Labels'] == 0, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]]

Cluster 2¶

toronto_merged.loc[toronto_merged['Cluster Labels'] == 1, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]]

Cluster 3¶

toronto_merged.loc[toronto_merged['Cluster Labels'] == 2, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]]

Cluster 4¶

toronto_merged.loc[toronto_merged['Cluster Labels'] == 3, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]]

Cluster 5¶

toronto_merged.loc[toronto_merged['Cluster Labels'] == 4, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]]

	Postcode	Borough	Neighborhood
0	M3A	North York	Parkwoods
1	M4A	North York	Victoria Village
2	M5A	Downtown Toronto	Regent Park
3	M6A	North York	Lawrence Heights, Lawrence Manor
4	M7A	Downtown Toronto	Queen's Park
5	M9A	Queen's Park (Toronto)	Queen's Park
6	M1B	Scarborough, Toronto	Rouge, Malvern
7	M3B	North York	Don Mills North
8	M4B	East York	Woodbine Gardens, Parkview Hill
9	M5B	Downtown Toronto	Ryerson, Garden District
10	M6B	North York	Glencairn
11	M9B	Etobicoke	Cloverdale, West Deane Park, Martin Grove, Isl...
12	M1C	Scarborough, Toronto	Port Union, Highland Creek , Rouge Hill
13	M3C	North York	Flemingdon Park, Don Mills South
14	M4C	East York	Woodbine Heights
15	M5C	Downtown Toronto	St. James Town
16	M6C	York	Humewood-Cedarvale
17	M9C	Etobicoke	Markland Wood, Eringate, Old Burnhamthorpe, Bl...
18	M1E	Scarborough, Toronto	Morningside, Guildwood, West Hill
19	M4E	East Toronto	The Beaches
20	M5E	Downtown Toronto	Berczy Park
21	M6E	York	Caledonia-Fairbanks
22	M1G	Scarborough, Toronto	Woburn
23	M4G	East York	Leaside
24	M5G	Downtown Toronto	Central Bay Street
25	M6G	Downtown Toronto	Christie
26	M1H	Scarborough, Toronto	Cedarbrae
27	M2H	North York	Hillcrest Village
28	M3H	North York	Wilson Heights, Bathurst Manor, Downsview North
29	M4H	East York	Thorncliffe Park
30	M5H	Downtown Toronto	Adelaide, King, Richmond
31	M6H	West Toronto	Dovercourt Village, Dufferin
32	M1J	Scarborough, Toronto	Scarborough Village
33	M2J	North York	Henry Farm, Fairview, Oriole
34	M3J	North York	Northwood Park, York University
35	M4J	East York	East Toronto
36	M5J	Downtown Toronto	Toronto Islands, Harbourfront East, Union Stat...
37	M6J	West Toronto	Little Portugal, Trinity–Bellwoods
38	M1K	Scarborough, Toronto	Kennedy Park, East Birchmount Park, Ionview
39	M2K	North York	Bayview Village
40	M3K	North York	Downsview East, CFB Toronto
41	M4K	East Toronto	Riverdale, The Danforth West
42	M5K	Downtown Toronto	Toronto Dominion Centre, Design Exchange
43	M6K	West Toronto	Brockton, Parkdale Village, Exhibition Place
44	M1L	Scarborough, Toronto	Golden Mile, Oakridge, Clairlea
45	M2L	North York	Silver Hills, York Mills
46	M3L	North York	Downsview
47	M4L	East Toronto	India Bazaar, The Beaches West
48	M5L	Downtown Toronto	Commerce Court, Victoria Hotel
49	M6L	North York	North Park, Downsview, Upwood Park
50	M9L	North York	Humber Summit
51	M1M	Scarborough, Toronto	Cliffcrest, Cliffside, Scarborough Village West
52	M2M	North York	Newtonbrook, Willowdale
53	M3M	North York	Downsview Central
54	M4M	East Toronto	Studio District
55	M5M	North York	Bedford Park, Lawrence Manor East
56	M6M	York, Toronto	Del Ray, Mount Dennis, Keelesdale, Silverthorn
57	M9M	North York	Humberlea, Emery
58	M1N	Scarborough, Toronto	Cliffside West, Birch Cliff
59	M2N	North York	Willowdale South
60	M3N	North York	Downsview Northwest
61	M4N	Central Toronto	Lawrence Park
62	M5N	Central Toronto	Roselawn
63	M6N	York, Toronto	The Junction North, Runnymede
64	M9N	York, Toronto	Weston
65	M1P	Scarborough, Toronto	Wexford Heights, Scarborough Town Centre, Dors...
66	M2P	North York	York Mills West
67	M4P	Central Toronto	Davisville North
68	M5P	Central Toronto	Forest Hill West, Forest Hill North
69	M6P	West Toronto	High Park, The Junction South
70	M9P	Etobicoke	Westmount
71	M1R	Scarborough, Toronto	Wexford, Maryvale
72	M2R	North York	Willowdale West
73	M4R	Central Toronto	North Toronto West
74	M5R	Central Toronto	The Annex, North Midtown, Yorkville
75	M6R	West Toronto	Roncesvalles, Parkdale
76	M7R	Mississauga	Canada Post Gateway Processing Centre
77	M9R	Etobicoke	Kingsview Village, Richview Gardens, Martin Gr...
78	M1S	Scarborough, Toronto	Agincourt
79	M4S	Central Toronto	Davisville
80	M5S	Downtown Toronto	University of Toronto, Harbord
81	M6S	West Toronto	Swansea, Runnymede
82	M1T	Scarborough, Toronto	Clarks Corners, Sullivan, Tam O'Shanter – Sull...
83	M4T	Central Toronto	Moore Park, Summerhill East
84	M5T	Downtown Toronto	Chinatown, Kensington Market, Grange Park
85	M1V	Scarborough, Toronto	Agincourt North, Steeles East, Milliken, Ontar...
86	M4V	Central Toronto	South Hill, Summerhill West, Deer Park, Forest...
87	M5V	Downtown Toronto	CN Tower, King and Spadina, Harbourfront West,...
88	M8V	Etobicoke	New Toronto, Mimico South, Humber Bay Shores
89	M9V	Etobicoke	Beaumond Heights, Albion Gardens, Silverstone,...
90	M1W	Scarborough, Toronto	L'Amoreaux West
91	M4W	Downtown Toronto	Rosedale
92	M5W	Downtown Toronto	Stn A PO Boxes 25 The Esplanade
93	M8W	Etobicoke	Alderwood, Long Branch
94	M9W	Etobicoke	Northwest
95	M1X	Scarborough, Toronto	Upper Rouge
96	M4X	Downtown Toronto	Cabbagetown, St. James Town
97	M5X	Downtown Toronto	Underground city, First Canadian Place
98	M8X	Etobicoke	The Kingsway, Old Mill North, Montgomery Road
99	M4Y	Downtown Toronto	Church and Wellesley
100	M7Y	East Toronto	Business Reply Mail Processing Centre 969 Eastern
101	M8Y	Etobicoke	Kingsway Park South East, Humber Bay, Old Mill...
102	M8Z	Etobicoke	Royal York South West, South of Bloor, The Que...

	Postcode	Borough	Neighborhood	Latitude	Longitude
0	M3A	North York	Parkwoods	43.753259	-79.329656
1	M4A	North York	Victoria Village	43.725882	-79.315572
2	M6A	North York	Lawrence Heights, Lawrence Manor	43.718518	-79.464763
3	M3B	North York	Don Mills North	43.745906	-79.352188
4	M6B	North York	Glencairn	43.709577	-79.445073
5	M3C	North York	Flemingdon Park, Don Mills South	43.725900	-79.340923
6	M2H	North York	Hillcrest Village	43.803762	-79.363452
7	M3H	North York	Wilson Heights, Bathurst Manor, Downsview North	43.754328	-79.442259
8	M2J	North York	Henry Farm, Fairview, Oriole	43.778517	-79.346556
9	M3J	North York	Northwood Park, York University	43.767980	-79.487262
10	M2K	North York	Bayview Village	43.786947	-79.385975
11	M3K	North York	Downsview East, CFB Toronto	43.737473	-79.464763
12	M2L	North York	Silver Hills, York Mills	43.757490	-79.374714
13	M3L	North York	Downsview	43.739015	-79.506944
14	M6L	North York	North Park, Downsview, Upwood Park	43.713756	-79.490074
15	M9L	North York	Humber Summit	43.756303	-79.565963
16	M2M	North York	Newtonbrook, Willowdale	43.789053	-79.408493
17	M3M	North York	Downsview Central	43.728496	-79.495697
18	M5M	North York	Bedford Park, Lawrence Manor East	43.733283	-79.419750
19	M9M	North York	Humberlea, Emery	43.724766	-79.532242
20	M2N	North York	Willowdale South	43.770120	-79.408493
21	M3N	North York	Downsview Northwest	43.761631	-79.520999
22	M2P	North York	York Mills West	43.752758	-79.400049
23	M2R	North York	Willowdale West	43.782736	-79.442259

	reasons.items	referralId	venue.categories	venue.id	venue.location.address	venue.location.cc	venue.location.city	venue.location.country	venue.location.distance	venue.location.formattedAddress	venue.location.labeledLatLngs	venue.location.lat	venue.location.lng	venue.location.state	venue.name	venue.photos.groups
0	[{'summary': 'This spot is popular', 'type': '...	e-0-4e8d9dcdd5fbbbb6b3003c7b-0	[{'id': '4bf58dd8d48988d163941735', 'name': 'P...	4e8d9dcdd5fbbbb6b3003c7b	Toronto	CA	Toronto	Canada	245	[Toronto, Toronto ON, Canada]	[{'label': 'display', 'lat': 43.75197604605557...	43.751976	-79.332140	ON	Brookbanks Park	[]
1	[{'summary': 'This spot is popular', 'type': '...	e-0-51d769f2498e3efc78bee6b9-1	[{'id': '4d954b0ea243a5684a65b473', 'name': 'C...	51d769f2498e3efc78bee6b9	NaN	CA	Toronto	Canada	230	[Toronto ON, Canada]	[{'label': 'display', 'lat': 43.75451313339121...	43.754513	-79.331942	ON	649 Variety	[]
2	[{'summary': 'This spot is popular', 'type': '...	e-0-4cb11e2075ebb60cd1c4caad-2	[{'id': '4bf58dd8d48988d1f9941735', 'name': 'F...	4cb11e2075ebb60cd1c4caad	29 Valley Woods Road	CA	Toronto	Canada	312	[29 Valley Woods Road, Toronto ON, Canada]	[{'label': 'display', 'lat': 43.75197441585782...	43.751974	-79.333114	ON	Variety Store	[]

	name	categories	lat	lng
0	Brookbanks Park	Park	43.751976	-79.332140
1	649 Variety	Convenience Store	43.754513	-79.331942
2	Variety Store	Food & Drink Shop	43.751974	-79.333114

	Neighborhood	Neighborhood Latitude	Neighborhood Longitude	Venue	Venue Latitude	Venue Longitude	Venue Category
0	Parkwoods	43.753259	-79.329656	Brookbanks Park	43.751976	-79.332140	Park
1	Parkwoods	43.753259	-79.329656	649 Variety	43.754513	-79.331942	Convenience Store
2	Parkwoods	43.753259	-79.329656	Variety Store	43.751974	-79.333114	Food & Drink Shop
3	Victoria Village	43.725882	-79.315572	Victoria Village Arena	43.723481	-79.315635	Hockey Arena
4	Victoria Village	43.725882	-79.315572	Tim Hortons	43.725517	-79.313103	Coffee Shop

	Neighborhood	Accessories Store	Airport	American Restaurant	Arts & Crafts Store	Asian Restaurant	Bakery	Bank	Bar	Baseball Field	Beer Store	Bike Shop	Boutique	Bridal Shop	Bubble Tea Shop	Burger Joint	Burrito Place	Bus Station	Butcher	Café	Candy Store	Caribbean Restaurant	Chinese Restaurant	Clothing Store	Coffee Shop	Comfort Food Restaurant	Concert Hall	Construction & Landscaping	Convenience Store	Cosmetics Shop	Deli / Bodega	Department Store	Dessert Shop	Dim Sum Restaurant	Diner	Discount Store	Dog Run	Electronics Store	Empanada Restaurant	Fast Food Restaurant	Food & Drink Shop	Food Court	Food Truck	French Restaurant	Fried Chicken Joint	Frozen Yogurt Shop	Furniture / Home Store	Gas Station	Gift Shop	Golf Course	Greek Restaurant	Grocery Store	Gym	Gym / Fitness Center	Hockey Arena	Home Service	Hotel	Ice Cream Shop	Indian Restaurant	Indonesian Restaurant	Intersection	Italian Restaurant	Japanese Restaurant	Juice Bar	Liquor Store	Lounge	Massage Studio	Mediterranean Restaurant	Metro Station	Middle Eastern Restaurant	Miscellaneous Shop	Movie Theater	Paper / Office Supplies Store	Park	Pet Store	Pharmacy	Pizza Place	Plaza	Pool	Portuguese Restaurant	Pub	Ramen Restaurant	Restaurant	Salon / Barbershop	Sandwich Place	Shoe Store	Shopping Mall	Smoothie Shop	Spa	Sporting Goods Shop	Steakhouse	Supermarket	Supplement Shop	Sushi Restaurant	Tea Room	Thai Restaurant	Theater	Toy / Game Store	Trail	Video Game Store	Video Store	Vietnamese Restaurant	Wings Joint	Women's Store
0	Bayview Village	0.000000	0.0	0.000000	0.000000	0.000000	0.00000	0.250000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.250000	0.000000	0.0	0.250000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.0	0.000000	0.000000	0.00000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.250000	0.000000	0.000000	0.000000	0.0	0.00	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000
1	Bedford Park, Lawrence Manor East	0.000000	0.0	0.041667	0.000000	0.000000	0.00000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.041667	0.041667	0.000000	0.0	0.000000	0.000000	0.083333	0.041667	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.0	0.083333	0.000000	0.00000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.041667	0.041667	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.041667	0.000000	0.000000	0.083333	0.000000	0.041667	0.041667	0.000000	0.0	0.00	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.041667	0.041667	0.000000	0.00	0.000000	0.041667	0.000000	0.041667	0.000000	0.083333	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.041667	0.000000	0.041667	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.041667
2	Don Mills North	0.000000	0.0	0.000000	0.000000	0.000000	0.00000	0.000000	0.0	0.200000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.200000	0.000000	0.2	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.0	0.000000	0.000000	0.00000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.20	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.200000	0.000000	0.000000	0.000000	0.0	0.00	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000
3	Downsview	0.000000	0.0	0.000000	0.000000	0.000000	0.00000	0.250000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.0	0.000000	0.000000	0.00000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.250000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.00	0.0	0.000000	0.000000	0.000000	0.000000	0.250000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.250000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000
4	Downsview Central	0.000000	0.0	0.000000	0.000000	0.000000	0.00000	0.000000	0.0	0.500000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.0	0.000000	0.000000	0.00000	0.5	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.00	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000
5	Downsview East, CFB Toronto	0.000000	0.5	0.000000	0.000000	0.000000	0.00000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.0	0.000000	0.000000	0.00000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.00	0.0	0.000000	0.000000	0.000000	0.000000	0.500000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000
6	Downsview Northwest	0.000000	0.0	0.000000	0.000000	0.000000	0.00000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.0	0.000000	0.000000	0.00000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.250000	0.250000	0.25	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.250000	0.000000	0.0	0.00	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000
7	Flemingdon Park, Don Mills South	0.000000	0.0	0.000000	0.000000	0.090909	0.00000	0.000000	0.0	0.000000	0.090909	0.045455	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.045455	0.000000	0.0	0.045455	0.045455	0.090909	0.000000	0.045455	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.045455	0.000000	0.045455	0.00	0.000000	0.0	0.045455	0.000000	0.00000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.090909	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.045455	0.045455	0.000000	0.000000	0.000000	0.0	0.00	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.045455	0.000000	0.045455	0.000000	0.000000	0.000000	0.000000	0.045455	0.000000	0.045455	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000
8	Glencairn	0.000000	0.0	0.000000	0.000000	0.000000	0.00000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.0	0.000000	0.000000	0.00000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.250000	0.000000	0.000000	0.000000	0.0	0.00	0.0	0.000000	0.000000	0.000000	0.000000	0.250000	0.000000	0.000000	0.250000	0.000000	0.00	0.000000	0.250000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000
9	Henry Farm, Fairview, Oriole	0.015625	0.0	0.015625	0.000000	0.015625	0.03125	0.015625	0.0	0.015625	0.000000	0.000000	0.015625	0.000000	0.000000	0.015625	0.015625	0.015625	0.000000	0.000000	0.015625	0.0	0.015625	0.156250	0.078125	0.000000	0.000000	0.000000	0.015625	0.015625	0.015625	0.015625	0.015625	0.000000	0.000000	0.000000	0.00	0.031250	0.0	0.078125	0.000000	0.03125	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.046875	0.015625	0.015625	0.000000	0.0	0.00	0.0	0.000000	0.000000	0.015625	0.000000	0.000000	0.000000	0.015625	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.015625	0.015625	0.015625	0.000000	0.015625	0.015625	0.015625	0.015625	0.000000	0.000000	0.015625	0.000000	0.015625	0.000000	0.015625	0.015625	0.00	0.015625	0.000000	0.000000	0.015625	0.031250
10	Hillcrest Village	0.000000	0.0	0.000000	0.000000	0.000000	0.00000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.25	0.000000	0.0	0.000000	0.000000	0.00000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.25	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.25	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.25	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000
11	Humber Summit	0.000000	0.0	0.000000	0.000000	0.000000	0.00000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.5	0.000000	0.000000	0.00000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.00	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.500000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000
12	Humberlea, Emery	0.000000	0.0	0.000000	0.000000	0.000000	0.00000	0.000000	0.0	0.333333	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.333333	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.0	0.000000	0.000000	0.00000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.00	0.0	0.000000	0.000000	0.000000	0.333333	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000
13	Lawrence Heights, Lawrence Manor	0.090909	0.0	0.000000	0.000000	0.000000	0.00000	0.000000	0.0	0.000000	0.000000	0.000000	0.090909	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.090909	0.090909	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.0	0.000000	0.000000	0.00000	0.0	0.000000	0.000000	0.000000	0.272727	0.000000	0.090909	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.00	0.0	0.000000	0.090909	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.090909	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.090909	0.000000	0.000000
14	North Park, Downsview, Upwood Park	0.000000	0.0	0.000000	0.000000	0.000000	0.25000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.250000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.0	0.000000	0.000000	0.00000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.00	0.0	0.000000	0.000000	0.000000	0.000000	0.250000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.25	0.000000	0.000000	0.000000	0.000000	0.000000
15	Northwood Park, York University	0.000000	0.0	0.000000	0.000000	0.000000	0.00000	0.000000	0.2	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.2	0.000000	0.000000	0.200000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.0	0.000000	0.000000	0.00000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.2	0.00	0.2	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000
16	Parkwoods	0.000000	0.0	0.000000	0.000000	0.000000	0.00000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.333333	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.0	0.000000	0.333333	0.00000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.00	0.0	0.000000	0.000000	0.000000	0.000000	0.333333	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000
17	Victoria Village	0.000000	0.0	0.000000	0.000000	0.000000	0.00000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.166667	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.0	0.000000	0.000000	0.00000	0.0	0.166667	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.166667	0.000000	0.000000	0.000000	0.000000	0.000000	0.166667	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.00	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.166667	0.000000	0.00	0.166667	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000
18	Willowdale South	0.000000	0.0	0.000000	0.027778	0.000000	0.00000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.027778	0.000000	0.000000	0.000000	0.000000	0.055556	0.000000	0.0	0.000000	0.000000	0.055556	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.027778	0.00	0.027778	0.0	0.027778	0.000000	0.00000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.027778	0.000000	0.00	0.000000	0.000000	0.027778	0.027778	0.000000	0.027778	0.000000	0.000000	0.055556	0.027778	0.000000	0.027778	0.0	0.00	0.0	0.027778	0.000000	0.027778	0.000000	0.000000	0.027778	0.000000	0.055556	0.027778	0.00	0.000000	0.000000	0.083333	0.055556	0.000000	0.055556	0.000000	0.027778	0.000000	0.000000	0.000000	0.027778	0.000000	0.000000	0.083333	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.027778	0.000000	0.000000
19	Willowdale West	0.000000	0.0	0.000000	0.000000	0.000000	0.00000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.166667	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.166667	0.00	0.000000	0.0	0.000000	0.000000	0.00000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.166667	0.000000	0.00	0.000000	0.166667	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.00	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.166667	0.166667	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000
20	Wilson Heights, Bathurst Manor, Downsview North	0.000000	0.0	0.000000	0.000000	0.000000	0.00000	0.047619	0.0	0.000000	0.000000	0.000000	0.000000	0.047619	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.047619	0.000000	0.095238	0.000000	0.000000	0.000000	0.000000	0.000000	0.047619	0.000000	0.000000	0.000000	0.047619	0.000000	0.00	0.000000	0.0	0.047619	0.000000	0.00000	0.0	0.000000	0.047619	0.047619	0.000000	0.047619	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.00	0.0	0.047619	0.000000	0.000000	0.000000	0.000000	0.047619	0.047619	0.047619	0.000000	0.00	0.000000	0.000000	0.000000	0.047619	0.000000	0.047619	0.000000	0.047619	0.000000	0.000000	0.000000	0.000000	0.047619	0.000000	0.047619	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.047619	0.000000	0.000000	0.000000
21	York Mills West	0.000000	0.0	0.000000	0.000000	0.000000	0.00000	0.250000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.250000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.0	0.000000	0.000000	0.00000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.00	0.0	0.000000	0.000000	0.000000	0.000000	0.500000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000

	Neighborhood	1st Most Common Venue	2nd Most Common Venue	3rd Most Common Venue	4th Most Common Venue	5th Most Common Venue	6th Most Common Venue	7th Most Common Venue	8th Most Common Venue	9th Most Common Venue	10th Most Common Venue
0	Adelaide, King, Richmond	Coffee Shop	Café	Steakhouse	Bar	Sushi Restaurant	Restaurant	Asian Restaurant	Thai Restaurant	Burger Joint	Bakery
1	Agincourt	Latin American Restaurant	Breakfast Spot	Lounge	Skating Rink	Yoga Studio	Dumpling Restaurant	Dog Run	Doner Restaurant	Donut Shop	Drugstore
2	Agincourt North, Steeles East, Milliken, Ontar...	Park	Playground	Eastern European Restaurant	Discount Store	Dog Run	Doner Restaurant	Donut Shop	Drugstore	Dumpling Restaurant	Electronics Store
3	Alderwood, Long Branch	Pizza Place	Gym	Skating Rink	Coffee Shop	Pharmacy	Sandwich Place	Pub	Pool	Dog Run	Dim Sum Restaurant
4	Bayview Village	Japanese Restaurant	Café	Bank	Chinese Restaurant	Discount Store	Doner Restaurant	Donut Shop	Drugstore	Dumpling Restaurant	Yoga Studio